Method and device for space-time estimation of one or more transmitters

ABSTRACT

Method and device to carry out space-time estimation of several transmitters in an antenna network, consisting of determining the various arrival angles θ mi  of the multipaths p m  transmitted by each transmitter.  
     Application to the goniometry of multipath sources.

[0001] This invention concerns a method for the space-time estimation of one or more transmitters in an antenna network when the wave transmitted by a transmitter is propagated as multipaths.

[0002] Multipaths exist when a wave transmitted by a transmitter is propagated along several paths towards a receiver or a goniometry system. Multipaths are due in particular to the presence of obstacles between a transmitter and a receiver.

[0003] The field of the invention concerns in particular that of the goniometry of radioelectric sources, the word source designating a transmitter. Goniometry means “the estimation of incidences”.

[0004] It is also that of spatial filtering whose purpose is to synthesise an antenna in the direction of each transmitter from a network of antennas.

[0005] The purpose of a traditional radiogoniometry system is to estimate the incidence of a transmitter, i.e. the angles of arrival of radioelectric waves incident on a network 1 of N sensors of a reception system 2, for example a network of several antennas as represented in FIGS. 1 and 2. The network of N sensors is coupled to a computation device 4 via N receivers in order to estimate the angles of incidence θ_(p) of the radioelectric waves transmitted by various sources p or transmitters and which are received by the network.

[0006] The wave transmitted by the transmitter can propagate along several paths according to a diagram given in FIG. 3. The wave k(θ_(d)) has a direct path with angle of incidence θ_(d) and the wave k(θ_(r)) a reflected path with angle of incidence θ_(r). The multipaths are due in particular to obstacles 5 located between the transmitter 6 and the reception system 7. At the reception station, the various paths arrive with various angles of incidence θ_(op) where p corresponds to the p^(th) path. The multipaths follow different propagation routes and are therefore received at different times t_(mp).

[0007] The N antennas of the reception system receive the signal x_(n)(t) where n is the index of the sensor. Using these N signals x_(n)(t), the observation vector is built: $\begin{matrix} {{\underset{\_}{x}(t)} = \begin{bmatrix} {x_{1}(t)} \\ . \\ . \\ {x_{n}(t)} \end{bmatrix}} & (1) \end{matrix}$

[0008] With M transmitters this observation vector x(t) is written as follows: $\begin{matrix} {{\underset{\_}{x}(t)} = {{\sum\limits_{m = 1}^{M}{\sum\limits_{p = 1}^{P_{m}}{\rho_{mp}{\underset{\_}{a}\left( \theta_{mp} \right)}{s_{m}\left( {t - \tau_{mp}} \right)}}}} + {\underset{\_}{b}(t)}}} & (2) \end{matrix}$

[0009] where

[0010] a(θ_(mp)) is the steering vector of the p^(th) path of the m^(th) transmitter. The vector a(θ) is the response of the network of N sensors to a source of incidence θ

[0011] ρ_(mp) is the attenuation factor of the p^(th) path of the m^(th) transmitter

[0012] τ_(mp) is the delay of the p^(th) path of the m^(th) transmitter

[0013] P_(m) is the number of multipaths of the m^(th) transmitter

[0014] s_(m)(t) is the signal transmitted by the m^(th) transmitter

[0015] b(t) is the noise vector composed of the additive noise b_(n)(t) (1≦n≦N) on each sensor.

[0016] The prior art describes various techniques of goniometry, of source separation and of goniometry after the separation of the source.

[0017] These techniques consist of estimating the signals s_(m)(t−τ_(mp)) from the observation vector x(t) with no knowledge of their time properties. These techniques are known as blind techniques. The only assumption is that the signals s_(m)(t−τ_(mp)) are statistically independent for a path p such that 1≦p≦P_(m) and for a transmitter m such that 1≦m≦M. Knowing that the correlation between the signals s_(m)(t) and s_(m)(t−τ) is equal to order 2 to the autocorrelation function r_(sm)(τ)=E[s_(m)(t)s_(m)(t−τ)*] of the signal s_(m)(t), we deduce that the multipaths of a given signal s_(m)(t) transmitter are dependent since the function r_(sm)(τ) is non null. However, two different transmitters m and m′, of respective signals s_(m)(t) and s_(m′)(t), are statistically independent if the relation E[s_(m)(t) s_(m′)(t)*]=0 is satisfied, where E[.] is the expected value. Under these conditions, these techniques can be used when the wave propagates in a single path, when P₁= . . . =P_(M)=1. The observation vector x(t) is then expressed by: $\begin{matrix} {{\underset{\_}{x}(t)} = {{{\sum\limits_{m = 1}^{M}{{\underset{\_}{a}\left( \theta_{m} \right)}{s_{m}(t)}}} + {\underset{\_}{b}(t)}} = {{A\quad {\underset{\_}{s}(t)}} + {\underset{\_}{b}(t)}}}} & (3) \end{matrix}$

[0018] where A=[a(θ₁) . . . a(θ_(M))] is the matrix of steering vectors of the sources and s(t) is the source vector such that s(t)=[s₁(t) . . . s_(M)(t)]^(T) (where the exponent T designates the transpose of vector u which satisfies in this case u=s(t)).

[0019] These methods consist of building a matrix W of dimension (N×M), called separator, generating at each time t a vector y(t) of dimension M which corresponds to a diagonal matrix and, to within one permutation matrix, to an estimate of the source vector s(t) of the envelopes of the M signals of interest to the receiver. This problem of source separation can be summarised by the following expression of the required vectorial output at time t of the linear separator W:

y(t)=W ^(H) x(t)=ΠΛŝ(t)  (4)

[0020] where Π and Λ correspond respectively to arbitrary permutation and diagonal matrices of dimension M and where ŝ(t) is an estimate of the vector s(t). W^(H) designates the transposition and conjugation operation of the matrix W.

[0021] These methods involve the statistics of order 2 and 4 of the observation vector x(t).

[0022] Order 2 Statistics: Covariance Matrix

[0023] The correlation matrix of the signal x(t) is defined by the following expression:

R _(xx) =E[x(t)x(t)^(H)]  (5)

[0024] Knowing that the source vector s(t) is independent of the noise b(t) we deduce from (3) that:

R _(xx) =AR _(ss) A ^(H)+σ² I  (6)

[0025] Where R_(ss)=E[s(t) s(t)^(H)] and E[b(t) b(t)^(H)]=σ²I.

[0026] The estimate of R_(xx) used is such that: $\begin{matrix} {{\hat{R}}_{xx} = {\frac{1}{T}{\sum\limits_{t = 1}^{T}{{\underset{\_}{x}(t)}{\underset{\_}{x}(t)}^{H}}}}} & (7) \end{matrix}$

[0027] where T corresponds to the integration period.

[0028] Order 4 Statistics: Quadricovariance

[0029] By extension of the correlation matrix, we define with order 4 the quadricovariance whose elements are the cumulants of the sensor signals x_(n)(t):

Qxx(i,j,k,l)=cum{xi(t), xj(t)*, xk(t)*, xl(t)}  (8)

[0030] With cum $\begin{matrix} \begin{matrix} \begin{matrix} {\left\{ {y_{i},y_{j},y_{k},y_{i}} \right\} = {{E\left\lbrack {y_{i}\quad y_{j}\quad y_{k}\quad y_{l}} \right\rbrack} - {{E\left\lbrack {y_{i}\quad y_{j}} \right\rbrack}\left\lbrack {y_{k}\quad y_{l}} \right\rbrack}}} \\ {\quad {- {{E\left\lbrack {y_{i}\quad y_{k}} \right\rbrack}\left\lbrack {y_{j}\quad y_{l}} \right\rbrack}}} \end{matrix} \\ {\quad {- {{E\left\lbrack {y_{i}\quad y_{l}} \right\rbrack}\left\lbrack {y_{j}\quad y_{k}} \right\rbrack}}} \end{matrix} & (9) \end{matrix}$

[0031] Knowing that N is the number of sensors, the elements Q_(xx)(i,j,k,l) are stored in a matrix Q_(xx) at line number N(j−1)+i and column number N(l−1)+k. Q_(xx) is therefore a matrix of dimension N²×N².

[0032] It is also possible to write the quadricovariance of observations x(t) using the quadricovariances of the sources and the noise written respectively Q_(ss) and Q_(bb). Thus according to expression (3) we obtain: $\begin{matrix} {Q_{xx} = {{\sum\limits_{i,j,k,l}{{{Q_{ss}\left( {i,j,k,l} \right)}\left\lbrack {{\underset{\_}{a}\left( \theta_{i} \right)} \otimes {\underset{\_}{a}\left( \theta_{j} \right)}^{*}} \right\rbrack}\left\lbrack {{\underset{\_}{a}\left( \theta_{k} \right)} \otimes {\underset{\_}{a}\left( \theta_{l} \right)}^{*}} \right\rbrack}^{H}} + Q_{bb}}} & (10) \end{matrix}$

[0033] where {circle over (x)} designates the Kronecker product such that: $\begin{matrix} {{\underset{\_}{u} \otimes \underset{\_}{v}} = {{\begin{bmatrix} {\underset{\_}{u}\quad v_{1}} \\ . \\ {\underset{\_}{u}\quad v_{K}} \end{bmatrix}\quad {where}\quad \underset{\_}{v}} = \begin{bmatrix} v_{1} \\ . \\ v_{K} \end{bmatrix}}} & (11) \end{matrix}$

[0034] Note that when there are independent sources, the following equality (12) is obtained: $Q_{xx} = {{\sum\limits_{m = 1}^{M}{{{Q_{ss}\left( {m,m,m,m} \right)}\quad\left\lbrack {{\underset{\_}{a}\left( \theta_{m} \right)} \otimes \left( {\underset{\_}{a}\left( \theta_{m} \right)} \right)^{*}} \right\rbrack}\quad\left\lbrack {{\underset{\_}{a}\left( \theta_{m} \right)} \otimes \left( {\underset{\_}{a}\left( \theta_{m} \right)} \right)^{*}} \right\rbrack}^{H}} + Q_{bb}}$

[0035] Since Q_(ss)(i,j,k,l)=0 for i≠j≠k≠l. In addition, in the presence of Gaussian noise the quadricovariance Q_(bb) of the noise cancels out and leads to (13): $Q_{xx} = {\sum\limits_{m = 1}^{M}{{{Q_{ss}\left( {m,m,m,m} \right)}\quad\left\lbrack {{\underset{\_}{a}\left( \theta_{m} \right)} \otimes \left( {\underset{\_}{a}\left( \theta_{m} \right)} \right)^{*}} \right\rbrack}\quad\left\lbrack {{\underset{\_}{a}\left( \theta_{m} \right)} \otimes \left( {\underset{\_}{a}\left( \theta_{m} \right)} \right)^{*}} \right\rbrack}^{H}}$

[0036] An example of a known source separation method is the Souloumiac-Cardoso method which is described, for example, in document [1] entitled “Blind Beamforming for Non Gaussian Signals”, authors J. F. CARDOSO, A. SOULOUMIAC, published in the review IEE Proc-F, Vol 140, No. 6, pp 362-370, December 1993.

[0037]FIG. 3 schematises the principle of this separation method based on the statistical independence of the sources: under these conditions, the matrices R_(ss) and Q_(ss) of expressions (6) and (10) are diagonal. This figure shows that the algorithm used to process the observation vector corresponding to the signals received on the sensor network is composed of a data x(t) whitening step 10 resulting in an observation vector z(t), and a steering vector identification step 11, possibly followed by a spatial filtering step 12 using the signal vector x(t) to obtain an estimated signal vector ŝ′(k). The whitening step uses the covariance matrix R_(xx) in order to orthonormalise the basis of the steering vectors a(θ_(l)) . . . a(θ_(M)). The second identification step uses the quadricovariance Q_(zz) to identify the steering vectors previously orthonormalised.

[0038] The coefficients of the spatial filtering step W_(i) are defined as follows

w _(i) =aR _(x) ⁻¹ a(θ_(i))

[0039] Whitening Step

[0040] Whitening is carried out to orthogonalise the mixture matrix A to be estimated. The observations x(t) must be multiplied by a matrix Θ⁻¹ such that the covariance matrix of denoised and whitened observations is equal to the identity matrix. z(t) represents the vector of noised and whitened observations:

z(t)=Θ⁻¹ x(t)=Θ⁻¹ As(t)+Θ⁻¹ b(t)  (14)

[0041] The matrix Θ of dimension N×M must then satisfy according to (6) the following relation:

ΘΘ^(H) =R _(xx) −R _(bb) =AR _(ss) A ^(H)  (15)

[0042] Knowing that E[b(t) b(t)^(H)]=σ² I, we deduce from (6) that the decomposition into eigenelements of R_(xx) satisfies:

R _(xx) =E _(s)Λ_(s) E _(s) ^(H)+σ² E _(b) E _(b) ^(H)  (16)

[0043] Where Λ_(s) is a diagonal matrix of dimension M×M containing the M largest eigenvalues of R_(xx). The matrix E_(s) of dimension N×M is composed of eigenvectors associated with the largest eigenvalues of R_(xx) and the matrix E_(b) of dimension N×(N−M) is composed of eigenvectors associated with the noise eigenvalue σ². Knowing firstly that R_(bb)=E[b(t) b(t)^(H)]=σ² I and that secondly by definition from the decomposition into eigenelements that (E_(s) E_(s) ^(H+E) _(b) E_(b) ^(H))=I, we deduce from (15) that:

ΘΘ^(H) =AR _(ss) A ^(H) =E _(s)(Λ_(s)−σ² I _(M))E _(s) ^(H)  (17)

[0044] We can then take for matrix Θ the following matrix of dimension N×M.

Θ=E _(s)(Λ_(s)−σ² I _(M))^(½)  (18)

[0045] According to (17) we deduce that the matrix Θ also equals:

Θ=AR _(ss) ^(½) U ^(H) with U ^(H) U=I _(M)  (19)

[0046] U is then a unit matrix whose columns are formed from orthonormed vectors. According to (3), (14) and (19) the vector z(t) of dimension M×1 can be expressed as follows:

z(t)=Us′(t)+Θ⁻¹ b(t) with s′(t)=R _(ss) ^(−½) s(t)  (20)

[0047] With decorrelated sources, the matrices R_(ss) and R_(ss) ^(−½) are diagonal and so the components of vectors s′(t) and s(t) are equal to within one amplitude such that: ${{\underset{\_}{s}}^{\prime}(t)} = {{\begin{bmatrix} {{s_{1}(t)}/\sqrt{\gamma_{1}}} \\ . \\ {{s_{M}(t)}/\sqrt{\gamma_{M}}} \end{bmatrix}\quad {where}\quad {\underset{\_}{s}(t)}} = {{\begin{bmatrix} {s_{1}(t)} \\ . \\ {s_{M}(t)} \end{bmatrix}\quad {and}\quad R_{ss}} = \begin{bmatrix} \gamma_{1} & . & 0 \\ . & . & . \\ 0 & . & \gamma_{M} \end{bmatrix}}}$

[0048] The matrix U is composed of whitened steering vectors such that:

U=[t _(l) . . . t _(M)]  (21)

[0049] Identification Step

[0050] The purpose of this step is to identify the unit matrix U composed of M whitened steering vectors t_(m). According to (20) and (21), the vector z(t) of whitened observations can be expressed as follows: $\begin{matrix} {{{\underset{\_}{z}(t)} = {{{U\quad {{\underset{\_}{s}}^{\prime}(t)}} + {\Theta^{- 1}{\underset{\_}{b}(t)}}} = {{\sum\limits_{m = 1}^{M}{{\underset{\_}{t}}_{M}{s_{m}^{\prime}(t)}}} + {{\underset{\_}{b}}^{\prime}(t)}}}}{\text{with}\quad {s_{m}^{\prime}(t)}} = {{{{s_{m}(t)}/\sqrt{\gamma_{m}}}\quad \text{and}\quad {{\underset{\_}{b}}^{\prime}(t)}} = {\Theta^{- 1}{{\underset{\_}{b}(t)}.}}}} & (22) \end{matrix}$

[0051] Knowing that the M signal sources s′_(m)(t) are independent, we deduce according to (13) that the quadricovariance of z(t) can be written as follows: $\begin{matrix} {Q_{zz} = {\sum\limits_{m = 1}^{M}{{{Q_{s^{\prime}s^{\prime}}\left( {m,m,m,m} \right)}\left\lbrack {{\underset{\_}{t}}_{m} \otimes {\underset{\_}{t}}_{m}^{*}} \right\rbrack}\left\lbrack {{\underset{\_}{t}}_{m} \otimes {\underset{\_}{t}}_{m}^{*}} \right\rbrack}^{H}}} & (23) \end{matrix}$

[0052] Under these conditions, the matrix Q_(zz) of dimension M²×M² has rank M. Diagonalisation of Q_(zz) then enables us to retrieve the eigenvectors associated with the M largest eigenvalues. These eigenvectors can be written as follows: $\begin{matrix} {{\underset{\_}{e}}_{m} = {{\sum\limits_{i = 1}^{M}{{\alpha_{mi}\left( {{\underset{\_}{t}}_{i} \otimes {\underset{\_}{t}}_{i}^{*}} \right)}\quad \text{for}\quad m}} = {1\quad \cdots \quad M}}} & (24) \end{matrix}$

[0053] We then transform each vector e_(m) of length M² into a matrix U_(m) of dimension (M×M) whose columns are the M M-uplets forming, the vector e_(m). $\begin{matrix} {{U_{m} = \begin{pmatrix} e_{m,1} & e_{m,{M + 1}} & \cdots & e_{m,{{{({M - 1})}M} + 1}} \\ \vdots & \vdots & \vdots & \vdots \\ e_{m,M} & e_{m,{2M}} & \cdots & e_{m,M^{2}} \end{pmatrix}}{{\text{with}\quad {\underset{\_}{e}}_{m}} = \begin{pmatrix} e_{m,1} \\ \vdots \\ e_{m,M} \\ \vdots \\ e_{m,{{{({M - 1})}M} + 1}} \\ \vdots \\ e_{\overset{.}{m},M^{2}} \end{pmatrix}}} & (25) \end{matrix}$

[0054] which according to equation (24) can also be written: $\begin{matrix} {U_{m} = {{\sum\limits_{i = 1}^{M}{\alpha_{mi}{\underset{\_}{t}}_{i}{\underset{\_}{t}}_{i}^{H}}} = {U\quad \delta_{m}U^{H}}}} & (26) \end{matrix}$

[0055] where δ_(m) is a diagonal matrix of elements α_(mi). To identify the matrix U, simply diagonalise the eigenmatrices U_(m) for 1≦m≦M since the matrix U is a unit matrix due to the whitening step. Reference [1] proposes an algorithm for joint diagonalisation of the M matrices U_(m).

[0056] Knowing the matrices U and Θ we can deduce according to (19) the matrix A of steering vectors such that:

ΘU=AR _(ss) ^(½) =[a′ _(l) . . . a′ _(M)] with a′ _(m) =a(θ_(m))×{square root}{square root over (γ_(m))}  (27)

[0057] We therefore identify the steering vectors a(θ_(m)) of the sources to within a multiplying factor {square root}{square root over (γ_(m))}. According to expressions (20) and (14) and knowing the matrices U and Θ we deduce the estimate of the source vector s′(t) such that: ${{\underset{\_}{\hat{s}}}^{\prime}(t)} = {U^{H}\Theta^{- 1}{\underset{\_}{x}(t)}}$ ${\text{where}\quad {{\underset{\_}{\hat{s}}}^{\prime}(t)}} = \begin{bmatrix} {{{\hat{s}}_{1}(t)}/\sqrt{\gamma_{1}}} \\ \vdots \\ {{{\hat{s}}_{M}(t)}/\sqrt{\gamma_{M}}} \end{bmatrix}$

[0058] knowing that $\begin{matrix} {R_{ss} = \begin{bmatrix} \gamma_{1} & \cdots & 0 \\ \vdots & \vdots & \vdots \\ 0 & \cdots & \gamma_{M} \end{bmatrix}} & (28) \end{matrix}$

[0059] We therefore estimate the signals s_(m)(t) to within a multiplying factor of value (1/{square root}{square root over (γ_(m))}) such that:

ŝ′ _(m)(t)=(1/{square root}{square root over (γ_(m))}) ŝ _(m)(t)  (29)

[0060] Behaviour with Multipaths

[0061] Knowing that, for a given transmitter m, the signals s_(m)(t) and s_(m)(t−τ) are correlated, it is possible to deduce the existence of dependence between the signal multipaths s_(m)(t−τ_(mp)) with 1<p<P_(m).

[0062] It was demonstrated in reference [2], authors P. CHEVALIER, V. CAPDEVIELLE, P. COMON, entitled “Behaviour of HO blind source separation methods in the presence of cyclostationary correlated multipaths”, published in the IEEE review SP Workshop on HOS, Alberta (Canada), July 1997, that the source separation method separates the transmitters without separating their multipaths. So by taking the signal model of expression (2) for the m^(th) transmitter we identify the following P_(m) vectors: $\begin{matrix} {{\underset{\_}{u}}_{mp} = {{\sum\limits_{i = 1}^{P_{m}}{\beta_{mpi}{\underset{\_}{a}\left( \theta_{mi} \right)}\quad \text{for}\quad 1}} \leq p \leq P_{m}}} & (30) \end{matrix}$

[0063] Similarly for the m^(th) transmitter we identify the following P_(m) signals: $\begin{matrix} {{{\hat{s}}_{mp}^{\prime}(t)} = {{\sum\limits_{i = 1}^{P_{m}}{\beta_{mpi}^{\prime}{{\hat{s}}_{m}\left( {t - \tau_{mi}} \right)}\quad \text{for}\quad 1}} \leq p \leq P_{m}}} & (31) \end{matrix}$

[0064] These source separation techniques assume, in order to be efficient, that the signals propagate in a single path. The signals transmitted by each transmitter are considered as statistically independent.

[0065] With a single path where P₁= . . . =P_(m)=1, the sources are all independent and the M vectors identified at separation output have, according to the relation (27), the following structure:

a′ _(m)=β_(m) a(θ_(m)) for 1≦m≦M  (32)

[0066] For each transmitter, the following noise projector Π_(bm) is built: $\begin{matrix} {\prod\limits_{bm}{= {{I_{N} - {\frac{{\underset{\_}{a}}_{m}^{\prime}{\underset{\_}{a}}_{m}^{\prime \quad H}}{{\underset{\_}{a}}_{m}^{\prime \quad H}{\underset{\_}{a}}_{m}^{\prime}}\quad \text{for}\quad 1}} \leq m \leq M}}} & (33) \end{matrix}$

[0067] By applying the MUSIC principle we then look for the incidence {circumflex over (θ)}_(m) of the m^(th) transmitter which cancels the following criterion: $\begin{matrix} {{\hat{\theta}}_{m} = {{\begin{matrix} \min \\ \theta \end{matrix}\left\{ {{\underset{\_}{a}(\theta)}^{H}{\prod\limits_{bm}{\underset{\_}{a}(\theta)}}} \right\} \quad \text{for}\quad 1} \leq m \leq M}} & (34) \end{matrix}$

[0068] The principle of the MUSIC algorithm is for example described in document [3] by R. O. Schmidt entitled “A signal subspace approach to multiple emitters location and spectral estimation”, PhD Thesis, Stanford University, CA, November 1981.

[0069] Thus, using vectors a′₁ . . . a′_(M) identified at source separation output it is possible to deduce the incidences {circumflex over (θ)}₁ . . . {circumflex over (θ)}_(M) for each transmitter. However, the sources must be decorrelated if the vectors identified are to satisfy the relation a′_(m)=β_(m) a (θ_(m)).

[0070] For example, in document [4] entitled “Direction finding after blind identification of sources steering vectors: The Blind-Maxcor and Blind-MUSIC methods”, authors P. CHEVALIER, G. BENOIT, A. FERREOL, and published in the review Proc. EUSIPCO, Triestre, September 1996, a blind-MUSIC algorithm of the same family as the MUSIC algorithm, known by those skilled in the art, is applied.

[0071] The known techniques of the prior art can therefore be used to determine the incidences for the various transmitters if the wave transmitted for each of these transmitters propagates as monopath.

[0072] The invention concerns a method and a device which can be used to determine in particular, for each transmitter propagating as multipaths, the incidences of the arrival angles for the multipaths.

[0073] The purpose of this invention is to carry out selective goniometry by transmitter in the presence of multipaths, i.e. for P_(m)>1.

[0074] One of the methods implemented by the invention is to group the signals received for each transmitter, before carrying out the goniometry of all these multipaths for each transmitter, for example.

[0075] Another method consists of space-time separation of the sources or transmitters.

[0076] In this description, the following terms are defined:

[0077] Ambiguities: we have an ambiguity when the goniometry algorithm can estimate with equal probability either the true incidence of the source or another quite different incidence. The greater the number of sources to be identified simultaneously, the greater the risk of ambiguity.

[0078] Multipath: when the wave transmitted by a transmitter propagates along several paths towards the goniometry system. Multipaths are due to the presence of obstacles between a transmitter and a receiver.

[0079] Blind: with no a priori knowledge of the transmitting sources.

[0080] The invention concerns a method for space-time estimation of the angles of incidence of one or more transmitters in an antenna network wherein it comprises at least a step to separate the transmitters and a step to determine the various arrival angles θ_(mi) of the multipaths p transmitted by each transmitter.

[0081] According to a first mode of realisation, the method comprises at least a step to separate the transmitters in order to obtain the various signals s(t) received by the antenna network, a step to group the various signals by transmitter and a step to determine the various angles θ_(mi) of the multipaths by transmitter.

[0082] The step to group the various signals by transmitter comprises for example:

[0083] a step to intercorrelate two by two the components u_(k)(t) of the signal vector s′(t) resulting from the source separation step,

[0084] a step to find the delay value(s) in order to obtain a maximum value for the intercorrelation function, r_(kk′)(τ)=E[u_(k)(t)u_(k′)(t−τ)*],

[0085] a step to store the various path indices for which the correlation function is a maximum.

[0086] The method comprises for example a step to determine delay times using the incidences θ_(n), the signal s_(m)(t) and the search for the maximum of the criterion Cri_(i)(δτ) to obtain δτ_(mi)=τ_(mi)−τ_(ml), with Cri_(i)(δτ)=E[s_(m)(t)(i)s_(m)(t−δτ)(1)*].

[0087] According to a second realisation variant, the method comprises a step of space-time separation of the various transmitters before determining the various arrival angles θ_(mi).

[0088] The space-time separation step comprises for example a step where, for a given transmitter, the signal s_(m)(t) is delayed, thereby comparing the delayed signal to the output of a filter of length L_(m), whose inputs are s_(m)(t) up to s_(m)(t−τ_(m)), before applying the source separation.

[0089] The method may comprise a step to identify and eliminate the outputs associated with the same transmitter after having determined the angles θ_(mi).

[0090] The method implements, for example, different types of goniometry, such as high resolution methods such as in particular MUSIC, interferometry methods, etc.

[0091] The method applies to the goniometry of multipath sources and also when P₁= . . . =P_(m)=1.

[0092] The invention also concerns a device to make a space-time estimation of a set of transmitters which transmit waves propagating as multipaths in a network of N sensors wherein it comprises a computer designed to implement the steps of the method characterised by the steps described above.

[0093] The method according to the invention can be used in particular to carry out separate goniometry of the transmitters. Thus, only the incidences of the multipaths of a given transmitter are determined.

[0094] Under these conditions, compared with a traditional technique which must simultaneously locate all the transmitters with their multipaths, the method can be used to perform goniometry on fewer sources.

[0095] Other advantages and features of the invention will be clearer on reading the following description given as a non-limiting example, with reference to figures representing in:

[0096]FIG. 1 a transmission-reception system, and in FIG. 2 a network of N sensors,

[0097]FIG. 3 the possible paths of waves transmitted by a transmitter,

[0098]FIG. 4 a source separation method according to the prior art,

[0099]FIG. 5 a diagram of the various steps of a first method according to the invention of source association and in FIG. 6 the associated processing algorithm,

[0100]FIG. 7 a second variant with space-time source separation,

[0101]FIG. 8 an example of the spectrum obtained.

[0102] The acquisition system described in FIG. 2 includes a network 2 composed of N antennas 3 ₁ to 3 _(N) linked to a computer 4 designed in particular to implement the various steps of the method according to the invention. The computer is for example adapted to determine the arrival angles for each transmitter 1 i (FIG. 1) transmitting a wave which can propagate as multipaths as shown on the diagram in FIG. 1. The computer is equipped with means to perform the goniometry of each transmitter 1 i.

[0103] The sensors or antennas of the receiver system receive signals x_(n)(t) as described previously, so that the observation vector x(t) can be built using the N antennas.

[0104] Remember that when there are multipaths, we identify for the m^(th) transmitter, P_(m) paths for the propagation of the transmitted wave and the following P_(m) vectors: ${\underset{\_}{u}}_{mp} = {{\sum\limits_{i = 1}^{P_{m}}{\beta_{mpi}{\underset{\_}{a}\left( \theta_{mi} \right)}\quad \text{for}\quad 1}} \leq p \leq P_{m}}$

[0105] where a(θ_(mi)) is the steering vector of the i^(th) path of the m^(th) transmitter.

[0106] Similarly for this m^(th) transmitter we identify the following P_(m) signals: ${{\hat{s}}_{mp}^{\prime}(t)} = {{\sum\limits_{i = 1}^{P_{m}}{\beta_{mpi}^{\prime}{{\hat{s}}_{m}\left( {t - \tau_{mi}} \right)}\quad \text{for}\quad 1}} \leq p \leq P_{m}}$

[0107] Also, for paths p≠p′, the signals ŝ′_(mp)(t) and ŝ′_(mp′)(t) output from a separation step are independent since they satisfy:

E[ŝ′ _(mp)(t)ŝ′ _(mp′)(t)*]=0.

[0108] The idea of the method according to the invention is to carry out separate goniometry of the transmitters when the wave or waves propagate as multipaths, either by grouping the various outputs by transmitter, or by introducing a space-time source separation step before performing the goniometry for each transmitter.

[0109] Method Associating the Source Separation Outputs by Transmitter

[0110] According to a first implementation of the method described in FIG. 5, the invention consists of detecting the outputs ŝ′_(mp)(t) issued from the separation step 20, of grouping 21 the outputs belonging to the same transmitter and of performing for example a goniometry step 22 on each output, for a given transmitter.

[0111] The method includes for example the following steps:

[0112] a) Separate, 20, the sources according to a separation method known by Professionals and applied to the observation vector x(t) received by the antenna network. After this separation step, the method has obtained a vector s′(t) of components ŝ′_(mp)(t) representative of the various paths transmitted by the m^(th) transmitter,

[0113] b) Group the outputs by transmitter, 21,

[0114] c) Correlate two by two the components of the vector s′(t) resulting from the source separation step,

[0115] d) Search for the value of the delay time τ_(mp) to obtain a maximum value of the intercorrelated signals, and store the corresponding paths, for example in a table step d).

[0116] e) Perform a goniometry step, 22, for each transmitter to obtain the angles θ_(mPm)

[0117] Following the separation step a), the vector s′(t) of component ŝ′_(mp)(t) can be written as follows: $\begin{matrix} {{{\hat{\underset{\_}{s}}}^{\prime}(t)} = {{\begin{bmatrix} {u_{1}(t)} \\ \vdots \\ {u_{K}(t)} \end{bmatrix}\quad \text{where}\quad K} = {{\sum\limits_{m = 1}^{M}{P_{m}\quad \text{and}\quad {u_{k}(t)}}} = {{\hat{s}}_{mp}^{\prime}(t)}}}} & (35) \end{matrix}$

[0118] According to expression (31), the function r_(kk′)(τ) of intercorrelation between the signals u_(k)(t) and u_(k′)(t) is non null when they are associated with the same transmitter.

r _(kk′)(τ)=E[u _(k)(t)u _(k′)(t−τ)*]≠0 for τ>0 and k≠k′  (36)

[0119] The signals u_(k)(t) and u_(k′)(t) are in fact different linear combinations of the signals ŝ_(m)(t−τ_(mp)) for (1≦p≦P_(m)). Knowing that after filtering these two signals are noised respectively by b_(k)(t) et b_(k′)(t), we obtain: ${u_{k}(t)} = {{\sum\limits_{i = 1}^{P_{m}}{\beta_{ki}^{\prime}{{\hat{s}}_{m}\left( {t - \tau_{mi}} \right)}}} + {b_{k}(t)}}$ ${u_{k^{\prime}}\left( {t - \tau} \right)} = {{\sum\limits_{i = 1}^{P_{m}}{\beta_{k^{\prime}i}^{\prime}{{\hat{s}}_{m}\left( {t - \tau_{mi} - \tau} \right)}}} + {b_{k^{\prime}}(t)}}$

[0120] The method comprises for example a step to search for the delay value which maximises the intercorrelation between the outputs u_(k)(t) and u_(k′)(t−τ) and a step to store the indices k and k′ when the maximum exceeds a threshold.

[0121] To test the correlation between u_(k)(t) and u_(k′)(t−τ), a Gardner type detection test can be applied, such as that described in the document entitled “Detection of the number of cyclostationary signals in unknows interference and noise”, authors S V. Schell and W. Gardner, published in Proc of Asilonan conf on signal, systems and computers 5-7 November 90.

[0122] To do this, the following detection criterion can be calculated: $\begin{matrix} {{{{V_{{kk}^{\prime}}(\tau)} = {{- 2}T\quad {\ln \left( {1 - \frac{{{\hat{r}}_{{kk}^{\prime}}}^{2}}{{\hat{r}}_{kk}{\hat{r}}_{k^{\prime}k^{\prime}}}} \right)}}}{\text{with}\quad {\hat{r}}_{{kk}^{\prime}}} = {{\frac{1}{T}{\sum\limits_{t = 1}^{T}{{u_{k}(t)}{u_{k^{\prime}}\left( {t - \tau} \right)}^{*}\quad \text{and}\quad {\hat{r}}_{kk}}}} = {\frac{1}{T}{\sum\limits_{t = 1}^{T}{{u_{k}(t)}}^{2}}}}}{{\text{then}\quad {\hat{r}}_{k^{\prime}k^{\prime}}} = {\frac{1}{T}{\sum\limits_{t = 1}^{T}{{u_{k^{\prime}}\left( {t - \tau} \right)}}^{2}}}}} & (37) \end{matrix}$

[0123] Knowing that in the assumption where u_(k)(t)=b_(k)(t) and u_(k′)(t)=b_(k′)(t) the criterion V_(kk′)(τ) obeys a chi-2 law with 2 degrees of freedom, we deduce the correlation test by defining a threshold α(pfa):

[0124] The signals u_(k)(t) and u_(k′)(t) are correlated (H₁): V_(kk′)(τ)>α(pfa)

[0125] The signals u_(k)(t) and u_(k′)(t) are decorrelated (H₀): V_(kk′)(τ)<α(pfa)

[0126] Knowing the probability law of the criterion V_(kk′)(τ) with noise only assumption, we then choose the threshold α(pfa) in a chi-2 table for a probability of exceeding a given threshold, with 2 degrees of freedom.

[0127]FIG. 6 summarises an example of implementing the grouping technique including the correlation test.

[0128] For a transmitter of given index m, the computer searches for the delay values τ for which the energy received by the antenna network is a maximum, by making a two by two intercorrelation of the signals resulting from the separation of sources 30, 31, 32, function r_(kk′)(τ). It stores the chosen paths, maximising the intercorrelation function, in a table Tk containing the indices of the chosen paths and the indices of the transmitters, 33, by using the above-mentioned correlation test and the threshold α(pfa), 34. Then the method eliminates the identical tables Tk, for example by simple comparison 35. The computer then determines the table Tm(p), 36, composed of the source separation output indices associated with the same transmitter. Following these steps, the computer can perform goniometry for each transmitter.

[0129] Following these steps, the computer determines the number M of transmitters and the number of paths Pm for each transmitter. The table Tm obtained contains the source separation output indices associated with the same transmitter.

[0130] Goniometry

[0131] Knowing the paths for each transmitter, the computer carries out, for example, a goniometry for each transmitter as described below.

[0132] For a transmitter composed of P_(m) paths we know, according to expression (30), that the P_(m) vectors identified further to source separation are all a linear combination of steering vectors a(θ_(mi)) of its multipaths. ${\underset{\_}{u}}_{k} = {{{\sum\limits_{i = 1}^{P_{m}}{\beta_{ki}{\underset{\_}{a}\left( \theta_{mi} \right)}\quad \text{for}\quad 1}} \leq p \leq {P_{m}\quad \text{knowing~~that}\quad {T_{m}(p)}}} = k}$

[0133] The table T_(m)(p) is composed of the source separation output indices associated with the same transmitter. With the P_(m) outputs u_(k) we therefore calculate the following covariance matrix: $\begin{matrix} {R_{xm} = {\frac{1}{P_{m}}{\sum\limits_{p = 1}^{P_{m}}{{\underset{\_}{u}}_{{Tm}{(p)}}{\underset{\_}{u}}_{{Tm}{(p)}}^{H}}}}} & (38) \end{matrix}$

[0134] To estimate the incidences of the multipaths θ_(ml) up to θ_(mPm), simply apply any goniometry algorithm on the covariance matrix R_(xm). In particular, we can apply a high resolution algorithm such as MUSIC described in reference [3]. Note that the blind-MUSIC algorithm of reference [4] is a special case of the latter algorithm when MUSIC is applied with P_(m)=1.

[0135] Estimation of the Delay Times of the Paths of the m^(th) Transmitter

[0136] According to an implementation variant of the invention, the method estimates the values of the delay times of the paths of the m^(th) transmitter.

[0137] In this paragraph, the incidences θ_(mi) of the P_(m) paths are assumed known for 1<i<P_(m), and the associated steering vectors a(θ_(mi)) deduced from these values. This information is used by the method to deduce the propagation delays between the various paths. We know that the vectors u_(mi) and the signals ŝ′_(mp)(t) obtained at source separation output satisfy the following relation: $\begin{matrix} {{\sum\limits_{i = 1}^{P_{m}}{\rho_{mi}{\underset{\_}{a}\left( \theta_{mi} \right)}{s_{m}\left( {t - \tau_{mi}} \right)}}} = {\sum\limits_{i = 1}^{P_{m}}{{\underset{\_}{u}}_{mi}{{\hat{s}}_{mi}^{\prime}(t)}}}} & (40) \end{matrix}$

[0138] where ρ_(mi) designates the attenuation factor of the i^(th) path. Expression (40) can be written in matrix form as follows:

A _(m) s _(m)(t)=U_(m) ŝ′ _(m)(t)  (41)

[0139] By putting: A_(m)=[a(θ_(ml)). . . . a(θ_(mPm))] and U_(m)=[u_(ml) . . . u_(mPm)]and ${{\underset{\_}{s}}_{m}(t)} = {{\begin{bmatrix} {\rho_{m1}{s_{m}\left( {t - \tau_{m1}} \right)}} \\ \vdots \\ {\rho_{mPm}{s_{m}\left( {t - \tau_{mPm}} \right)}} \end{bmatrix}\quad \text{then}\quad {{\hat{\underset{\_}{s}}}_{m}^{\prime}(t)}} = \begin{bmatrix} {{\hat{s}}_{m1}(t)} \\ \vdots \\ {{\hat{s}}_{mPm}(t)} \end{bmatrix}}$

[0140] Knowing the matrix A_(m) of steering vectors of the multipaths and the matrix U_(m) of vectors identified, we deduce the vector s_(m)(t) according to the vector ŝ′_(m)(t) resulting from the source separation such that:

s _(m)(t)=A _(m) ⁻¹ U _(m) ŝ′ _(m)(t)  (42)

[0141] Knowing that the i^(th) component of s_(m)(t) satisfies s_(m)(t)(i)=ρ_(mi) s_(m)(t−τ_(mi)) we maximise the following criterion to estimate the delay δτ_(mi)=τ_(mi)−τ_(ml) of the i^(th) path with respect to the 1^(st) path. $\begin{matrix} {{{Cri}_{i}\left( {\delta \quad \tau} \right)} = {{{E\left\lbrack {{s_{m}(t)}(i){s_{m}\left( {t - {\delta \quad \tau}} \right)}(1)^{*}} \right\rbrack}\quad \text{such~~~that}\quad \max \quad \left( {{Cri}\left( {\underset{\delta \quad \tau}{\delta}\quad \tau} \right)} \right)} = {\delta \quad \tau_{m\quad i}}}} & (43) \end{matrix}$

[0142] The algorithm to estimate the δτ_(mi) for 1i<P_(m) consists of performing the following steps:

[0143] Step No. 1: Construct A_(m)=[a(θ_(ml)) . . . . a(θ_(mPm))] from incidences θ_(mi).

[0144] Step No. 2: Calculate the signal s_(m)(t) using the expression (42).

[0145] Step No. 3: For each path perform the following operations

[0146] Calculate the criterion Cri_(i)(δτ) of expression (43)

[0147] Maximise the criterion Cri_(i)(δτ) to obtain δτ_(mi)=τ_(mi)−τ_(ml).

[0148] Space-time Source Separation

[0149] A second method to implement the invention consists for example of applying the source separation on the space-time observation y(t)=[x(t)^(T) . . . x(t−L+1)^(T)]^(T).

[0150]FIG. 7 represents an example of a diagram to execute the steps of the method.

[0151] By using the space-time observation, it is possible to apply directly, on each source separation output, a goniometry of the multipaths of one of the transmitters. Since one transmitter is associated with several outputs, the method includes a step to eliminate the outputs associated with the transmitter on which the goniometry process has just been carried out.

[0152] This second realisation variant can be used in particular to locate more sources than with the first method. According to this second variant in fact, the limiting parameter is the number of transmitters (M<N) whereas previously, the limiting parameter is the total number of multipaths, i.e. ΣP_(m)<N.

[0153] In this second implementation variant, the signal model used is that of expression (2). Firstly, the method includes a step 40 to model the signals s_(m)(t) by considering them as finite pulse response signals of length L_(m). Under these conditions, the delayed signal s_(m)(t−τ_(mp)) is the output of a filter of length L_(m), whose inputs are s_(m)(t) up to s_(m)(t−L_(m)+1), i.e.: $\begin{matrix} {{s_{m}\left( {t - \tau_{mp}} \right)} = {\sum\limits_{k = 1}^{L_{m}}{{h_{mp}(k)}{s_{m}\left( {t - k + 1} \right)}}}} & (44) \end{matrix}$

[0154] By taking expression (2) again, we obtain the following expression for x(t): $\begin{matrix} {{{\underset{\_}{x}(t)} = {{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{L_{m}}{{{\underset{\_}{h}}_{m}(k)}{s_{m}\left( {t - k} \right)}}}} + {\underset{\_}{b}(t)}}}{{\text{with}\quad {{\underset{\_}{h}}_{m}(k)}} = {\sum\limits_{p = 1}^{P_{m}}{\rho_{mp}{\underset{\_}{a}\left( \theta_{mp} \right)}{h_{mp}(k)}}}}} & (45) \end{matrix}$

[0155] The space-time observation y(t) associated with x(t) can be written as follows (46): ${\underset{\_}{y}(t)} = {\begin{bmatrix} {\underset{\_}{x}(t)} \\ \vdots \\ {\underset{\_}{x}\left( {t - L + 1} \right)} \end{bmatrix} = {{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{L_{m} + L}{{{\underset{\_}{h}}_{m}^{L}(k)}{s_{m}\left( {t - k} \right)}}}} + {{\underset{\_}{b}}^{L}(t)}}}$ ${\text{with}\quad {{\underset{\_}{b}}^{L}(t)}} = \begin{bmatrix} {\underset{\_}{b}(t)} \\ \vdots \\ {\underset{\_}{b}\left( {t - L + 1} \right)} \end{bmatrix}$

[0156] Knowing that: ${\underset{\_}{x}\left( {t - 1} \right)} = {{\sum\limits_{m = 1}^{M}{\sum\limits_{k = {1 + l}}^{L_{m} + l}{{{\underset{\_}{h}}_{m}\left( {k - l} \right)}{s_{m}\left( {t - k} \right)}}}} + {\underset{\_}{b}(t)}}$

[0157] Ignoring the edge effects, we deduce that: $\begin{matrix} {{{\underset{\_}{h}}_{m}^{L}(k)} = {\begin{bmatrix} {{\underset{\_}{h}}_{m}(k)} \\ \vdots \\ {\underset{\_}{h}\left( {k - L + 1} \right)} \end{bmatrix} = {\sum\limits_{p = 1}^{P_{m}}{\rho_{mp}\begin{bmatrix} {{\underset{\_}{a}\left( \theta_{mp} \right)}{h_{mp}(k)}} \\ \vdots \\ {{\underset{\_}{a}\left( \theta_{mp} \right)}{h_{mp}\left( {k - L + 1} \right)}} \end{bmatrix}}}}} & (47) \end{matrix}$

[0158] Source Separation and Goniometry on the Space-time Observation y(t):

[0159] The method, for example, implements directly, 41, the Souloumiac-Cardoso source separation method described in the afore-mentioned reference [1] on the observation vector y(t). Knowing that the signals s_(m)(t−k) are correlated, according to equations (46), (30) and reference [2], the method identifies for each transmitter several space-time signatures u_(mk) satisfying: $\begin{matrix} {{\underset{\_}{u}}_{mk} = {{\sum\limits_{i = 1}^{L_{m} + 1}{\beta_{mki}{{\underset{\_}{h}}_{m}^{L}(i)}}} = {{\sum\limits_{p = 1}^{P_{m}}{\rho_{mp}{\sum\limits_{i = 1}^{L_{m} + L}{{\beta_{mki}\begin{bmatrix} {{\underset{\_}{a}\left( \theta_{mp} \right)}{h_{mp}(i)}} \\ \vdots \\ {{\underset{\_}{a}\left( \theta_{mp} \right)}{h_{mp}\left( {i - L + 1} \right)}} \end{bmatrix}}\quad 1}}}} \leq k \leq K}}} & (48) \end{matrix}$

[0160] Thus, for a transmitter with a pulse response of length L_(m) we identify for this transmitter at source separation output [1] K vectors which are a linear combination of vectors [a(θ_(mp))^(T)h_(mp)(i) . . . a(θ_(mp))^(T)h_(mp)(i−L+1)]T dependent on the incidences θ_(mp) of the multipath of this transmitter.

[0161] Goniometry of the m^(th) Transmitter on the Vector u_(mk), 42,

[0162] The method then carries out, 42, a goniometry of a transmitter on one of the vectors u_(ml) . . . u_(mk) identified at source separation output. Under these conditions the vector u_(mk) is transformed into the following matrix (49): ${\underset{\_}{u}}_{mk} = {\left. \begin{bmatrix} u_{1} \\ \vdots \\ u_{N} \\ \vdots \\ u_{1 + {N{({L - 1})}}} \\ \vdots \\ u_{N + {N{({L - 1})}}} \end{bmatrix}\Rightarrow U_{mk} \right. = \begin{bmatrix} u_{1} & \quad & u_{1 + {N{({L - 1})}}} \\ \vdots & \vdots & \vdots \\ u_{N} & \quad & u_{N + {N{({L - 1})}}} \end{bmatrix}}$

[0163] According to the relation (48) U_(mk) is expressed by: $\begin{matrix} {U_{mk} = {{\sum\limits_{p = 1}^{P_{m}}{\rho_{mp}{\underset{\_}{a}\left( \theta_{mp} \right)}{\sum\limits_{i = 1}^{L_{m} + L}{\beta_{mki}{{\underset{\_}{t}}_{mp}^{L}(i)}^{T}\quad \text{such~~that}\quad {{\underset{\_}{t}}_{mp}^{L}(i)}}}}} = {\quad\begin{bmatrix} {h_{mp}(i)} \\ \vdots \\ {h_{mp}\left( {i - L + 1} \right)} \end{bmatrix}}}} & (50) \end{matrix}$

[0164] The computer can then estimate the incidences θ_(ml) . . . θ_(mPm) of the paths of the m^(th) transmitter by applying a goniometry algorithm on the covariance matrix R_(mk)=U_(mk) U_(mk) ^(†)for example a high resolution algorithm such as MUSIC described in reference [3], authors R. O. Schmidt, entitled “A signal subspace approach to multiple emitters location and spatial estimation”, PhD Thesis, Stanford University, CA, November 1991.

[0165] Identification and Elimination of Outputs u_(mk) (1<k<K) Associated with the Same Transmitter, 43,

[0166] After performing the goniometry on the vector u_(mk), the computer will eliminate the vectors associated with the same transmitter i.e. the u_(mk) for j≠k. The matrices U_(m′j) associated with the u_(m′j) all satisfy the following relation according to (50):

U _(m′j) =A _(m′) B _(m′j) ^(T) with A _(m′=[) a(θ_(m′l)) . . . a(θ_(m′Pm′))]  (51)

[0167] Where $B_{m^{\prime}j} = {{\left\lbrack {{\underset{\_}{b}}_{1j}\quad \ldots \quad {\underset{\_}{b}}_{{Pm}^{\prime}j}} \right\rbrack \quad {\underset{\_}{b}}_{pj}} = {\rho_{m^{\prime}p}{\sum\limits_{i = 1}^{L_{m} + L}{\beta_{m^{\prime}{ji}}{{\underset{\_}{t}}_{m^{\prime}p}^{L}(i)}}}}}$

[0168] Knowing that the projector Π_(m)=I−A_(m) (A_(m) ^(H) A_(m))⁻¹ A_(m) ^(H) satisfies Π_(m) A_(m)=0 we deduce that (52):

[0169] Cri(j)=trace{Π_(m) U_(m′j) (U_(m′j) ^(H) U_(m′j))⁻¹U_(m′j) ^(H) Π_(m)}=trace{Π_(m) P_(m′j) Π_(m)}=0

[0170] when m=m′

[0171] Where trace{Mat} designates the trace of the matrix Mat. For any matrix U_(m′j), the criterion Cri(j) is normalised between 0 and 1. When the matrix U_(mj) is associated with a vector u_(mj) associated with the same transmitter as u_(mk) this criterion Cri(j) is cancelled. Knowing that Π_(m) and U_(mj) are estimated with a certain accuracy, we will compare Cri(j) with a threshold cc close to zero (typically 0.1) to decide whether the vectors u_(mk) and u_(m′j) belong to the same transmitter: Cri(j)<α implies that u_(mk) and u_(m′j) are associated with the same transmitter and that m′=m. So to identify that a vector u_(m′j) is associated with the same transmitter as the vector u_(mk) the computer performs the following operations:

[0172] 1) After the goniometry on u_(mk) the computer determines the incidences θ_(ml) . . . θ_(mPm) of the paths and builds the matrix A_(m) of the expression (51). We deduce that Π_(m)=I−A_(m) (A_(m) ^(H) A_(m))⁻¹ A_(m) ^(H).

[0173] 2) it transforms according to relation (49) the vector u_(m′j) into matrix U_(m′j). We deduce that P_(m′j)=U_(m′j) (U_(m′j) ^(H) U_(m′j))⁻¹U_(m′j) ^(H).

[0174] 3) it calculates the criterion Cri(j)=trace{Π_(m)P_(m′j) Π_(m) } of the equation (52)

[0175] 4) it applies for example the following association test:

[0176] Cri(j)<α

u_(mk) and u_(m′j) are associated with the same transmitter m′=m and elimination of vector u_(mj).

[0177] Cri(j)>α

m′≠m.

[0178] This operation is carried out on all different vectors of u_(mk), then the computer moves to the next transmitter, carrying out the goniometry step on one of the vectors u_(m′j) not eliminated. After the goniometry, the method repeats the elimination and all these operations continue until there are no more vectors u_(m′j).

[0179] Estimation of the Delay Times of the Paths of the m^(th) Transmitter

[0180] According to an implementation variant of the method, the incidences θ_(mi) of the P_(m) paths are known for 1<i<P_(m) and the associated steering vectors a(θ_(mi)) can be deduced. This information is used to deduce the propagation delays between the paths. To obtain the path delay values, the computer uses the outputs of the space-time source separation. According to expression (2) we may write the expression (46) of y(t) as follows: $\begin{matrix} {{\underset{\_}{y}(t)} = {\begin{bmatrix} {\underset{\_}{x}(t)} \\ \vdots \\ {\underset{\_}{x}\left( {t - L + 1} \right)} \end{bmatrix} = {{\sum\limits_{m = 1}^{M}{\sum\limits_{i = 1}^{P_{m}}{\rho_{mi}\begin{bmatrix} {{\underset{\_}{a}\left( \theta_{mi} \right)}{s_{m}\left( {t - \tau_{mi}} \right)}} \\ \vdots \\ {{\underset{\_}{a}\left( \theta_{mi} \right)}{s_{m}\left( {t - L + 1 - \tau_{mi}} \right)}} \end{bmatrix}}}} + {{\underset{\_}{b}}^{L}(t)}}}} & (53) \end{matrix}$

[0181] We therefore deduce that the vectors u_(mk) according to expression (48) and the signals ŝ′_(mk)(t) obtained at source separation output and associated with the m^(th) transmitter satisfy the following relation: $\begin{matrix} {{\sum\limits_{i = 1}^{P_{m}}\quad {\rho_{mi}\begin{bmatrix} {{\underset{\_}{\alpha}\left( \theta_{mi} \right)}{s_{m}\left( {t - \tau_{mi}} \right)}} \\ . \\ . \\ . \\ {{\underset{\_}{\alpha}\left( \theta_{mi} \right)}{s_{m}\left( {t - L + 1 - \tau_{mi}} \right)}} \end{bmatrix}}} = {\sum\limits_{k = 1}^{K}\quad {{\underset{\_}{u}}_{mk}{\hat{s}}_{mk}^{\prime}}}} & (54) \end{matrix}$

[0182] where ρ_(mi) designates the attenuation factor of the i^(th) path. Knowing that u_(mk) ⁰ corresponds to the first N components of u_(mk) we deduce that: $\begin{matrix} {{\sum\limits_{i = 1}^{P_{m}}\quad {\rho_{mi}{\underset{\_}{a}\left( \theta_{mi} \right)}{s_{m}\left( {t - \tau_{mi}} \right)}}} = {\sum\limits_{k = 1}^{K}{{\underset{\_}{u}}_{mk}^{0}\quad \cdot {{\hat{s}}_{mk}^{\prime}(t)}}}} & (55) \end{matrix}$

[0183] Expression (55) can be written in matrix form as follows:

A _(m) s _(m)(t)=U _(m) ⁰ ŝ _(m)(t)  (56)

[0184] By putting: A_(m)=[a(θ_(ml)) . . . a(θ_(mPm))] and U_(m) ⁰=[u_(m) ⁰ . . . u_(mK) ⁰ ]and ${{\underset{\_}{s}}_{m}(t)} = {{\begin{bmatrix} {\rho_{m1}{s_{m}\left( {t - \tau_{mi}} \right)}} \\ . \\ {\rho_{mPm}{s_{m}\left( {t - \tau_{mPm}} \right)}} \end{bmatrix}\quad {then}\quad {{\hat{\underset{\_}{s}}}_{m}^{\prime}(t)}} = \begin{bmatrix} {{\hat{s}}_{m1}(t)} \\ . \\ {{\hat{s}}_{mK}(t)} \end{bmatrix}}$

[0185] Knowing the matrix A_(m) of steering vectors of the multipaths and the matrix U_(m) ⁰ of vectors identified, we deduce the vector s_(m)(t) according to the vector ŝ′_(m)(t) resulting from the space-time source separation such that:

s _(m)(t)=A _(m) ⁻¹ U _(m) ⁰ ŝ′ _(m)(t)  (57)

[0186] Knowing that the i^(th) component of s_(m)(t) satisfies s_(m)(t)(i)=ρ_(mi) s_(m)(t−τ_(mi)) we maximise the following criterion to estimate the delay δτ_(mi)=τ_(mi)−τ_(ml) of the i^(th) path with respect to the 1^(st) path. $\begin{matrix} {{{Cri}_{i}\left( {\delta \quad \tau} \right)} = {{{E\left\lbrack {{s_{m}(t)}(i){s_{m}\left( {t - {\delta \quad \tau}} \right)}(1)^{*}} \right\rbrack}\quad \text{such~~that}\quad \max \quad \left( {{Cri}\left( {\underset{\delta \quad \tau}{\delta}\quad \tau} \right)} \right)} = {\delta \quad \tau_{m\quad i}}}} & (58) \end{matrix}$

[0187] The method then executes the algorithm to estimate the δτ_(mi) for 1<i<P_(m) consisting for example of performing the following steps:

[0188] Step No. 1: Construct A_(m)=[a(θ_(ml)) . . . a(θ_(mPm))] from incidences θ_(mi).

[0189] Step No. 2: Calculate the signal s_(m)(t) using the expression (57).

[0190] Step No. 3: For each path perform the following operations.

[0191] Calculate the criterion Cri_(i)(δτ) of expression (58).

[0192] Maximise the criterion Cri_(i)(δτ) to obtain δτ_(mi)=τ_(mi)−τ_(ml).

[0193] Unlike the first method described above, in this case we use the K outputs u_(mk) and ŝ′_(mk)(t) (1≦k≦K) of the space-time source separation. In addition, we only use the 1st N components of u_(mk′)i.e. the vector u_(mk) ⁰(N number of antennas).

[0194] Simulation Example

[0195] In this example, we simulate the case of M=2 transmitters where one of the transmitters is composed of two paths. The two transmitters have the following characteristics in terms of incidences, delays and paths:

[0196] 1^(st) transmitter (m=1): composed of P₁=2 paths

[0197] such that θ₁₁=60° θ₁₂32 75° and the delay τ₁₂=0 τ₁₂=2 samples QPSK NRZ with 10 samples by symbols.

[0198] 2^(nd) transmitter (m=2): composed of P₁₌2 paths

[0199] such that θ₂₁=150 and delay τ₂₁₌0

[0200]FIG. 8 represents the MUSIC pseudo-spectra on the matrices R_(1i)(curve I) and R_(2i) (curve II) associated respectively with the first and second transmitters. The maxima of these pseudo-spectra can be used to determine the incidences θ_(mp) of the multipaths of these transmitters. 

1. Method for space-time estimation of one or more transmitters using an antenna network wherein it comprises at least a step to separate the transmitters and a step to determine the various arrival angles θ_(mi) of the multipaths p_(m) transmitted by each transmitter:
 2. Method according to claim 1, wherein it comprises at least a step to separate the transmitters in order to obtain the various signals s(t) received by the antenna network, a step to group the various signals for each transmitter and a step to determine the various angles θ_(mi) of the multipaths for each transmitter.
 3. Method according to claim 2, wherein the step to group the various signals by transmitter comprises: a step to intercorrelate two by two the components u_(k)(t) of the signal vector s′(t) resulting from the source separation step, a step to find the delay value(s) in order to obtain a maximum value for the intercorrelation function, r_(kk′)(τ)=E[u_(k)(t)u_(k′)(t−τ)*], a step to store the various path indices for which the correlation function is a maximum.
 4. Method according to one of claims 1 to 3, wherein it comprises a step to determine delay times using the incidences θmi, the signal s_(m)(t) and the search for the maximum of the criterion Cri_(i)(δτ) to obtain δτ_(mi)=τ_(mi)−τ_(ml), with Cri _(i)(δτ)=E[s _(m)(t)(i)s _(m)(t−δτ)(1)*].
 5. Method according to claim 1, wherein it comprises a step of space-time separation of the various transmitters before determining the various arrival angles θ_(mi).
 6. Method according to claim 5, wherein the space-time separation step comprises a step where, for a given transmitter, the signal s_(m)(t) is delayed, thereby comparing the delayed signal to the output of a filter of length L_(m), whose inputs are s_(m)(t) up to s_(m)(t−τ_(m)), before applying the source separation step.
 7. Method according to claim 5 or 6, wherein it comprises a step to identify and to eliminate the outputs associated with the same transmitter after having determined the angles θ_(mi).
 8. Method according to one of claims 2 to 3 and 5 to 7, wherein it uses a MUSIC type high resolution goniometry method, by interferometry, etc.
 9. Application of the method according to one of claims 1 to 8 to the goniometry of multipath sources.
 10. Application of the method according to one of claims 1 to 8 to the goniometry of paths with P₁=P₂= . . . =P_(m=)1.
 11. Device to carry out space-time estimation of a set of transmitters which transmit waves propagating as multipaths in a network of N sensors wherein it comprises a computer designed to implement the steps of the method according to one of claims 1 to
 7. 